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All Journal International Journal of Electrical and Computer Engineering Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Informatika
Agung Wahyu Setiawan
Institut Teknologi Bandung
Computer Science & IT Electrical & Electronics Engineering

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Classification of Palm Oil Fresh Fruit Bunch using Multiband Optical Sensors Agung Wahyu Setiawan; Richard Mengko; Ayu Paranitha H. Putri; Donny Danudirdjo; Alfie Rizky Ananda
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (664.376 KB) | DOI: 10.11591/ijece.v9i4.pp2386-2393

Abstract

This study investigated optical sensor system consist of sixteen light emitting diode (LED) in visible/near infrared region to detect palm oil fresh fruit bunch (FFB) quality. Practically, experience grader assessed FFB quality by its ripeness based on external features such as colour and number of detached fruitlets. However, different seed and plantation management resulting in FFB quality variation. Same external features not linearly correlate with FFB oil content that corresponding with industrial needs. The 660 nm LED is choosen to be used to estimate the oil content of FFB. Using linear discriminant analysis (LDA) with Mahalanobis distance, the accuracy of the systems is 79.8% and 88.2%. From 33 FFB oil content measurement, grader misclassified 4 out of 17 FFB as ripe FFB but with low oil content (<17.5%) and misclassified 7 out of 16 FFB as unripe but with high oil content (>=17.5%). Classifying model build from FFB from main plantation then tested to evaluate FFB from smallholder. Classification model generated from FFB oil content data showed more accurate result compared to model generated from visual inspection 66.7% compared to 52.1%. Model accuracies attained by Discriminant Analysis (DA) and k-Nearest Neighbors (k-NN) were 79.8% and 80.7%, respectively based on grader evaluation. Model accuracies based on FFB oil content was 88.2% for both classifying algorithms.
A Hierarchical Description-based Video Monitoring System for Elderly Mochamad Irwan Nari; Agung Wahyu Setiawan; Widyawardana Adiprawita
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (369.686 KB) | DOI: 10.11591/eecsi.v4.1044

Abstract

The increase in the number of elderly motivates academic researchers to develop technologies that can ensure self- sufficiency in their lives. In this research, prototype of an inexpensive video monitoring system for the elderly using a single RGB camera proposed. In the process is divided into two, namely vision and event recognition module. For event recognition, we use a hierarchical description-based approach with three attributes, namely posture (e.g., stand, sit and lie), location (e.g., walking zone, relaxing zone and toilet zone) and duration (e.g., short and long). Output this system is description activity recognized in the text. The experiment result shows our system can provide the effectiveness of the context description.
Avoiding Machine Learning Becoming Pseudoscience in Biomedical Research Meredita Susanty; Ira Puspasari; Nilam Fitriah; Dimitri Mahayana; Tati Erawati Latifah Rajab; Hasballah Zakaria; Agung Wahyu Setiawan; Rukman Hertadi
Jurnal Informatika Vol 10, No 1 (2023): April 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v10i1.12787

Abstract

The use of machine learning harbours the promise of more accurate, unbiased future predictions than human beings on their own can ever be capable of. However, because existing data sets are always utilized, these calculations are extrapolations of the past and serve to reproduce prejudices embedded in the data. In turn, machine learning prediction result raises ethical and moral dilemmas. As mirrors of society, algorithms show the status quo, reinforce errors, and are subject to targeted influences – for good and the bad. This phenomenon makes machine learning viewed as pseudoscience. Besides the limitations, injustices, and oracle-like nature of these technologies, there are also questions about the nature of the opportunities and possibilities they offer. This article aims to discuss whether machine learning in biomedical research falls into pseudoscience based on Popper and Kuhn's perspective and four theories of truth using three study cases. The discussion result explains several conditions that must be fulfilled so that machine learning in biomedical does not fall into pseudoscience
Co-Authors Alfie Rizky Ananda Ayu Paranitha H. Putri Dimitri Mahayana Donny Danudirdjo Ira Puspasari Meredita Susanty Mochamad Irwan Nari Nilam Fitriah Richard Mengko RUKMAN HERTADI Tati Latifah Erawati Rajab Widyawardana Adiprawita Zakaria, Hasballah